1. EA Trends In The Age Of The Customer
Максим Тамбиев
Региональный директор, Россия

2. Agenda
The state of EA in 2014
EA in the age of the customer
How enterprise architects engage with four business imperatives
New EA roles and practices
Is “Data-Driven” good enough?

3. Agenda
The state of EA in 2014
EA in the age of the customer
How EAs engage with four business imperatives
New EA roles and practices
Is “Data-Driven” good enough?

4. Key business themes drive enterprise architecture
Increasing demand
Driving maturity
Changing approach
Data-driven
Source: Q Global State Of Strategic Planning, Enterprise Architecture And PMO Online Survey

5. Priorities have shifted — sometimes the wrong way
Base: Enterprise architecture professionals; Source: September 2009 Global State Of Enterprise Architecture Online Survey; September 2010 Global State Of Enterprise Architecture Online Survey; September 2011 Global State Of Enterprise Architecture Online Survey; Q Global State Of Enterprise Architecture Online Survey; Q Global State Of Enterprise Architecture Online Survey; and Q Global State Of Strategic Planning, Enterprise Architecture And PMO Online Survey

6. EA remains in a technology management box
Source: Q Global State Of Strategic Planning, Enterprise Architecture And PMO Online Survey

7. Agenda
The state of EA in 2014
EA in the age of the customer
How enterprise architects engage with four business imperatives
New EA roles and practices
Is “Data-Driven” good enough?

8. Your business priorities are shifting

9. EA’s focus must shift from “boxes” to the customer life cycle
Growth and retention strategies
Customer insights
DISCOVER
Technology decisions
ENGAGE
EXPLORE
ASK
BUY
USE

10. Agenda
The state of EA in 2014
EA in the age of the customer
How enterprise architects engage with four business imperatives
New EA roles and practices
Is “Data-Driven” good enough?

11. Enterprise architects are a key resource for the age of customer
Transform the customer experience.
Embrace the mobile mind shift.
Customer-facing business processes
Outside-in architecture practices
SoE architecture
Digital platform strategy
Age of the customer
Turn big data into business insights.
Become a digital disruptor.
Data governance 2.0
Systems of insight
Renewed integration architecture
Innovation
Ecosystem orientation
EA operating model

12. Dynamic ecosystems of value require architectural investments
Systems of design
Systems of engagement
Systems of record
Systems of operations/ automation
Partners
Regulators
Customers
Sensors
Systems of insight
Systems of Design - PLM, PIM, MasterDataManagement? Every product, that is heavily touched by digital transformation. Design&Co-design of products…
Systems of operations - when you have a lot of assets to deploy. Example – utilities (smart grid).

13. Agenda
The state of EA in 2014
EA in the age of the customer
How enterprise architects engage with four business imperatives
New EA roles and practices
Is “Data-Driven” good enough?

14. Digital business will lead to the rise of two new architect roles
Trusted machines – privacy, security, agility and timeness

15. Digital customer experience architects bridge worlds
Source: November 14, 2014, “Predictions 2015: Customer Experience And Digital Business Rise In EA’s Agenda” Forrester report

16. Support project execution
EA leaders will struggle to meet new business demands with the same old approach to EA
Yesterday’s EA
High-performance EA
Inside-out approach
Outside-in approach
Technology-focused
Business-oriented
Artifact production
Stakeholder (customers) satisfaction
Project-driven
Strategy-driven
Standardization
Innovation
Cost
Business agility
Value
Align business impact of technology decisions, and enable focus on business strategy to execution.
Value
Support project execution

17. Some EA programs break out of the box . . .
“Driving Innovation with Enterprise Architecture” “Our Customers and our Digital Future” “Shifting Dell from hardware to solutions” “Business Capability Management” “Re-chartering EA for business outcomes” “Delivering Remarkable Customer Experience with Enterprise Architecture”

18. Agenda
The state of EA in 2014
EA in the age of the customer
How enterprise architects engage with four business imperatives
New EA roles and practices
Is “Data-driven” good enough?

19. ¾ EAs aspire to make their enterprise “data-driven”
Enterprise architects want to help their firms become data-driven, according to Forrester's recent Q Global State Of Strategic Planning, Enterprise Architecture, And PMO Online Survey (see Figure 1). On the surface, this seems like a good thing. Which is why, for example, more than 400 data experts and business decision-makers attended the daylong "Data-Driven Business" session at Strata + Hadoop World 2015 in San Jose. (see endnote 1) But our survey indicates that troubles are brewing.
Data management remains an IT department thing. Sixty-two percent of survey respondents said their IT departments still do most data management work. (see endnote 2) Why? Because data management is hard, and most businesspeople still prefer it to be somebody else's problem — like yours.
There's a lot of talk Sixty percent of survey respondents agreed that talk of analytics is usually about tools and technologies. Furthermore, 50% said they're spending a fair amount of time talking about emerging big data technologies. (see endnote 3) Why? Because many businesspeople fail to see the benefits.
. . . but few results. When it comes to action, only 29% said they're good at translating the results of analytics into measurable business outcomes. Similarly, only 29% said they consistently measure results and learn what works. (see endnote 4) Why? Because big data market buzz only addresses finding insight in data, not turning insight into action.
And little is happening to change this. Thirty-four percent said their architecture includes technologies, like API management tools, that connect insight to action. And only 23% of Agile software teams include data scientists and analytics experts. (see endnote 5) Why? Because architects are not using the right language to help their businesses connect the dots between data and action.

20. Pitfalls of the “data-driven” approach
Most data-driven initiatives fail to address important insight-to-execution requirements
How to test insights for value against the most important outcomes
How to deploy insights into the software your customer and employees use
How to ensure that you're continuously learning and refining your insights
Business analysts and data professionals care deeply about the age of the customer. For example, 75% of the global data and analytics decision-makers we surveyed responded that improving customer experience was a high or critical business priority in the coming year, while 69% percent reported a similar prioritization of addressing rising customer expectations. (see endnote 6) But business executives want insight — not data or analytics — to improve digital experiences, satisfaction, and loyalty outcomes. The problem is that data and analytics aspirations aren't consistently leading to positive results (see Figure 2).
Architects with data-driven aspirations seek to solve tough problems with data management, governance, and analytics performance. These are good things, but most data-driven initiatives fail to address important insight-to-execution requirements:
How to test insights for value against the most important outcomes. Abe Gong, a former data scientist for Jawbone and now an independent consultant, explained, "Data experts tend to make a lot of assumptions that predicting something will make a big difference to business. But often we don't see the expected impacts. You have to test a lot."
How to deploy insights into the software your customer and employees use. Sixty-one percent of enterprise architects surveyed said the output of analytics is primarily reports and dashboards. (see endnote 7) Insights users must find these, then figure out what they should do with the information. On the other hand, IBM's new Insights as a Service business unit works to embed insights directly into business processes and then sell users the insights on a consumption basis.
How to ensure that you're continuously learning and refining your insights. Stitch Fix, a nontraditional retailer that neither has a store nor sells clothes online, is a master at this. It constantly learns what customers do and don't like and updates its models. This way, Stitch Fix can efficiently ship clothes to people who don't like to shop. And its customers love it.

21. An insight-driven approach focuses your attention in the right place
Begin with the end in mind — effective action
Discuss potential insights
Ask, "How will we know what worked?" and "How will we learn?"
Identify and prioritize the right data and analytics problems
Talking about systems of insight keep conversations from drifting prematurely to data problems and technology black holes, from which they often never emerge. Instead, get traction with your business by (see Figure 3):
Beginning with the end in mind — effective action. Your business wants action to change important outcomes in its favor. One executive echoed this common theme: "I know that 50% of my marketing spend is going to the wrong things. I just don't know which 50%." If you can help executives like this figure out where and how to make better marketing allocations, you'll have their rapt attention.
Discussing potential insights. What insight might influence customer decisions in the right direction? Brainstorm with data and analytics professionals in your business to identify hypotheses. What data might lead to this insight? What processes do they use to make decisions? What software systems support the processes? How might you deploy the insight, such as via an alert, message, rules change, or embedded visualization?
Asking, "How will we know what worked?" and "How will we learn?" For a minute, set aside the huge pile of data and technology problems you know you need to solve. Take one more step. Work with your business to develop a strategy for systematically learning and refining the potential insights you uncovered. This is crucial. Understand that even if your initial assumptions are wrong, a closed-loop system of insight will ensure that you get better over time.
Identifying and prioritizing the right data and analytics problems. Many architects we speak with know they need a data hub, lake, or service layer. They are also aware that marketing is protective over a bunch of databases and tools, and they know about engineering's Hadoop cluster. They're painfully aware of the need for good master and reference data. By starting from the customer action and insight and working backward, you'll be able to articulate why you need to solve hard data and technology problems.
BUILD A FEW SYSTEMS OF INSIGHT BEFORE GOING FOR ENTERPRISE CAPABILITY
Enterprise solutions are part of your DNA as an enterprise architect. Naturally, you see all the big data projects spinning up, and you start thinking about a grand platform that's going to enable them all. Assad Shaik, chief data scientist at PNC Financial Services, told us, "A majority of IT organizations go to the business with a technology solution and show them why it's best in the market, then try to convince the business to use it rather than understanding the real business data challenges."
Stop this behavior. Rather, recognize the reality that each department has unique insight needs and is already solving them in some way. Help them assemble an insights team, provision them with data, support the tools they use or want, and help them deliver insights in the software and processes that create measurable outcomes. Once you've done this a few times, the need for enterprise capabilities will become clear. And the resources to solve them will be closely tied to board-level outcomes.

22. Maxim Tambiev
Country Manager, Russia
Forrester Research